The present invention relates to a method for optimizing resource allocation having general linear constraints and a nonlinear objective function and an apparatus using the method. The invention relates especially to a work scheduling apparatus which efficiently and automatically produces a superior plan as to plant construction scheduling while satisfying various given conditions.
Methods using linear programming or a branch and bound method as to general combinatorial optimization problems are described in "Solution of Large Scale 0-1 Integer Planning Problem", by Ellis L. Jhonson et al., Operations Research, Vol. 33, No. 4, July-August, 1985. Further, methods as to maximizing a positive definite quadratic function are described in "Nonlinear Programming Method", by H. Konno and H. Yamashita, NIKKAGIREN, 1978.
An apparatus for work scheduling support which has the aim of leveling worker resource stacks by using a constrained network management method was proposed in Japanese Patent Laid-Open No. 162463 (1990). And as to work progress managing, Japanese Patent Laid-Open No. 58169 (1991), was proposed. The above-mentioned methods describe constraints among tasks in a network and successively improve an evaluating function for leveling worker resource stacks in the network.
In conventional methods for solving combinatorial optimization problems based on the branch and bound method using successive search procedures, the Monte-Carlo method and the generic algorithm which takes a statistical approach are not effectual for a the large scale problem because use of them is restricted to comparatively small scale problems due to their time consuming computation. Generally, conventional combinatorial optimization algorithms can be applied only to smaller scale problems having hundreds of variables, while linear programming which treats continuous variables is applicable to large scale problems having tens of thousands of variables. Because conventional combinatorial optimization methods successively check for every searched point, they consume a great amount of computing time for a large scale planning problem.